TY - JOUR
T1 - Machine-Learning-Aided Mission-Critical Internet of Underwater Things
AU - Hou, Xiangwang
AU - Wang, Jingjing
AU - Fang, Zhengru
AU - Zhang, Xin
AU - Song, Shenghui
AU - Zhang, Xudong
AU - Ren, Yong
N1 - Publisher Copyright:
© 1986-2012 IEEE.
PY - 2021/7/1
Y1 - 2021/7/1
N2 - With people paying more attention to marine resources, the Internet of Things (IoT) has been extended to underwater, promoting the development of the Internet of Underwater Things (IoUT). Various compelling IoUT applications bring a new age to maritime activities. However, some mis-sion-critical maritime activities, including ocean earthquake forecasting, underwater navigation, and so on, pose a substantial challenge on existing IoUT architectures and relevant techniques. Therefore, in this article, to empower these implacable maritime activities, we conceive the concept of mission-critical IoUT and highlight its key features and challenges. Furthermore, to satisfy the stringent requirements of mission-critical IoUT, we propose a future maritime network architecture and machine-learning-aided key techniques in terms of information sensing, transmission, and processing. Moreover, we present our recent research on reliable and low-latency underwater information transmission. Finally, we provide the open issues and potential research trends for future mission-critical IoUT.
AB - With people paying more attention to marine resources, the Internet of Things (IoT) has been extended to underwater, promoting the development of the Internet of Underwater Things (IoUT). Various compelling IoUT applications bring a new age to maritime activities. However, some mis-sion-critical maritime activities, including ocean earthquake forecasting, underwater navigation, and so on, pose a substantial challenge on existing IoUT architectures and relevant techniques. Therefore, in this article, to empower these implacable maritime activities, we conceive the concept of mission-critical IoUT and highlight its key features and challenges. Furthermore, to satisfy the stringent requirements of mission-critical IoUT, we propose a future maritime network architecture and machine-learning-aided key techniques in terms of information sensing, transmission, and processing. Moreover, we present our recent research on reliable and low-latency underwater information transmission. Finally, we provide the open issues and potential research trends for future mission-critical IoUT.
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000688521300034
UR - https://openalex.org/W3194924098
UR - https://www.scopus.com/pages/publications/85113406830
U2 - 10.1109/MNET.011.2000684
DO - 10.1109/MNET.011.2000684
M3 - Journal Article
SN - 0890-8044
VL - 35
SP - 160
EP - 166
JO - IEEE Network
JF - IEEE Network
IS - 4
M1 - 9520368
ER -